Source code for datalad.distribution.dataset

# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 noet:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#   See COPYING file distributed along with the datalad package for the
#   copyright and license terms.
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Implements class Dataset

import logging
from os.path import (
    join as opj,
from weakref import WeakValueDictionary
import wrapt

from datalad import cfg
from datalad.config import ConfigManager
from datalad.core.local.repo import repo_from_path
from import AnnexRepo
from import Constraint
# DueCredit
from import due
from import duecredit_dataset
from import (
from import path_based_str_repr
from import (
from import PathBasedFlyweight
from import path as op

import datalad.utils as ut
from datalad.utils import (
    # TODO remove after a while, when external consumers have adjusted
    # to use get_dataset_root()
    get_dataset_root as rev_get_dataset_root,

lgr = logging.getLogger('datalad.dataset')
lgr.log(5, "Importing dataset")

[docs]@path_based_str_repr class Dataset(object, metaclass=PathBasedFlyweight): """Representation of a DataLad dataset/repository This is the core data type of DataLad: a representation of a dataset. At its core, datasets are (git-annex enabled) Git repositories. This class provides all operations that can be performed on a dataset. Creating a dataset instance is cheap, all actual operations are delayed until they are actually needed. Creating multiple `Dataset` class instances for the same Dataset location will automatically yield references to the same object. A dataset instance comprises of two major components: a `repo` attribute, and a `config` attribute. The former offers access to low-level functionality of the Git or git-annex repository. The latter gives access to a dataset's configuration manager. Most functionality is available via methods of this class, but also as stand-alone functions with the same name in `datalad.api`. """ # Begin Flyweight _unique_instances = WeakValueDictionary() @classmethod def _flyweight_preproc_path(cls, path): """Custom handling for few special abbreviations for datasets""" path_ = path if path in ('^', '^.'): dsroot = get_dataset_root(curdir) if dsroot is None: raise NoDatasetFound('No dataset contains path: {}'.format( str(Path.cwd()))) if path == '^': # get the topmost dataset from current location. Note that 'zsh' # might have its ideas on what to do with ^, so better use as -d^ path_ = Dataset(dsroot).get_superdataset( topmost=True).path elif path == '^.': # the dataset containing current directory path_ = dsroot elif path == '///': # TODO: logic/UI on installing a default dataset could move here # from search? path_ = cfg.obtain('datalad.locations.default-dataset') if path != path_: lgr.debug("Resolved dataset alias %r to path %r", path, path_) return path_ @classmethod def _flyweight_postproc_path(cls, path): # we want an absolute path, but no resolved symlinks if not op.isabs(path): path = op.join(op.getpwd(), path) # use canonical paths only: return op.normpath(path) def _flyweight_invalid(self): """Invalidation of Flyweight instance Dataset doesn't need to be invalidated during its lifetime at all. Instead the underlying *Repo instances are. Dataset itself can represent a not yet existing path. """ return False # End Flyweight def __hash__(self): # the flyweight key is already determining unique instances # add the class name to distinguish from strings of a path return hash((self.__class__.__name__, self.__weakref__.key))
[docs] def __init__(self, path): """ Parameters ---------- path : str or Path Path to the dataset location. This location may or may not exist yet. """ self._pathobj = path if isinstance(path, ut.Path) else None if isinstance(path, ut.PurePath): path = str(path) self._path = path self._repo = None self._id = None self._cfg = None self._cfg_bound = None
@property def pathobj(self): """pathobj for the dataset""" # XXX this relies on the assumption that self._path as managed # by the base class is always a native path if not self._pathobj: self._pathobj = ut.Path(self._path) return self._pathobj def __eq__(self, other): if not hasattr(other, 'pathobj'): return False # Ben: # It's pointing to the same thing, while not being the same object # (in opposition to the *Repo classes). So `ds1 == ds2`, # `but ds1 is not ds2.` I thought that's a useful distinction. On the # other hand, I don't think we use it anywhere outside tests yet. me_exists = self.pathobj.exists() other_exists = other.pathobj.exists() if me_exists != other_exists: # no chance this could be the same return False elif me_exists: # check on filesystem return self.pathobj.samefile(other.pathobj) else: # we can only do lexical comparison. # this will fail to compare a long and a shortpath. # on windows that could actually point to the same thing # if it would exists, but this is how far we go with this. return self.pathobj == other.pathobj def __getattr__(self, attr): # Assure that we are not just missing some late binding # @datasetmethod . We will use interface definitions. # The gotcha could be the mismatch between explicit name # provided to @datasetmethod and what is defined in interfaces meth = None if not attr.startswith('_'): # do not even consider those from datalad.interface.base import ( get_interface_groups, get_api_name, load_interface ) groups = get_interface_groups(True) for group, _, interfaces in groups: for intfspec in interfaces: # lgr.log(5, "Considering interface %s", intfspec) name = get_api_name(intfspec) if attr == name: meth_ = load_interface(intfspec) if meth_: lgr.debug("Found matching interface %s for %s", intfspec, name) if meth: lgr.debug( "New match %s possibly overloaded previous one %s", meth_, meth ) meth = meth_ if not meth: lgr.debug("Found no match among known interfaces for %r", attr) return super(Dataset, self).__getattribute__(attr) def close(self): """Perform operations which would close any possible process using this Dataset """ repo = self._repo self._repo = None if repo: # might take care about lingering batched processes etc del repo @property def path(self): """path to the dataset""" return self._path @property def repo(self): """Get an instance of the version control system/repo for this dataset, or None if there is none yet (or none anymore). If testing the validity of an instance of GitRepo is guaranteed to be really cheap this could also serve as a test whether a repo is present. Note, that this property is evaluated every time it is used. If used multiple times within a function it's probably a good idea to store its value in a local variable and use this variable instead. Returns ------- GitRepo or AnnexRepo """ # If we already got a *Repo instance, check whether it's still valid; # Note, that this basically does part of the testing that would # (implicitly) be done in the loop below again. So, there's still # potential to speed up when we actually need to get a new instance # (or none). But it's still faster for the vast majority of cases. # # TODO: Dig deeper into it and melt with new instance guessing. This # should also involve to reduce redundancy of testing such things from # within Flyweight.__call__, AnnexRepo.__init__ and GitRepo.__init__! # # Also note, that this could be forged into a single big condition, but # that is hard to read and we should be well aware of the actual # criteria here: if self._repo is not None and self.pathobj.resolve() == self._repo.pathobj: # we got a repo and path references still match if isinstance(self._repo, AnnexRepo): # it's supposed to be an annex # Here we do the same validation that Flyweight would do beforehand if there was a call to AnnexRepo() if self._repo is AnnexRepo._unique_instances.get( self._repo.path, None) and not self._repo._flyweight_invalid(): # it's still the object registered as flyweight and it's a # valid annex repo return self._repo elif isinstance(self._repo, GitRepo): # it's supposed to be a plain git # same kind of checks as for AnnexRepo above, but additionally check whether it was changed to have an # annex now. # TODO: Instead of is_with_annex, we might want the cheaper check for an actually initialized annex. # However, that's not completely clear. On the one hand, if it really changed to be an annex # it seems likely that this happened locally and it would also be an initialized annex. On the # other hand, we could have added (and fetched) a remote with an annex, which would turn it into # our current notion of an uninitialized annex. Question is whether or not such a change really # need to be detected. For now stay on the safe side and detect it. if self._repo is GitRepo._unique_instances.get( self._repo.path, None) and not self._repo._flyweight_invalid() and not \ self._repo.is_with_annex(): # it's still the object registered as flyweight, it's a # valid git repo and it hasn't turned into an annex return self._repo # Note: Although it looks like the "self._repo = None" assignments # could be used instead of variable "valid", that's a big difference! # The *Repo instances are flyweights, not singletons. self._repo might # be the last reference, which would lead to those objects being # destroyed and therefore the constructor call would result in an # actually new instance. This is unnecessarily costly. try: self._repo = repo_from_path(self._path) except ValueError: lgr.log(5, "Failed to detect a valid repo at %s", self.path) self._repo = None return if # TODO: Figure out, when exactly this is needed. Don't think it # makes sense to do this for every dataset, # no matter what => we want .repo to be as cheap as it gets. # Makes sense only on installed dataset - @never_fail'ed duecredit_dataset(self) return self._repo @property def id(self): """Identifier of the dataset. This identifier is supposed to be unique across datasets, but identical for different versions of the same dataset (that have all been derived from the same original dataset repository). Note, that a plain git/git-annex repository doesn't necessarily have a dataset id yet. It is created by `Dataset.create()` and stored in .datalad/config. If None is returned while there is a valid repository, there may have never been a call to `create` in this branch before current commit. Note, that this property is evaluated every time it is used. If used multiple times within a function it's probably a good idea to store its value in a local variable and use this variable instead. Returns ------- str This is either a stored UUID, or `None`. """ return self.config.get('', None) @property def config(self): """Get an instance of the parser for the persistent dataset configuration. Note, that this property is evaluated every time it is used. If used multiple times within a function it's probably a good idea to store its value in a local variable and use this variable instead. Returns ------- ConfigManager """ # OPT: be "smart" and avoid re-resolving .repo -- expensive in DataLad repo = self.repo if repo is None: # if there's no repo (yet or anymore), we can't read/write config at # dataset level, but only at user/system level # However, if this was the case before as well, we don't want a new # instance of ConfigManager if self._cfg_bound in (True, None): self._cfg = ConfigManager(dataset=None, dataset_only=False) self._cfg_bound = False else: self._cfg = repo.config self._cfg_bound = True return self._cfg def recall_state(self, whereto): """Something that can be used to checkout a particular state (tag, commit) to "undo" a change or switch to a otherwise desired previous state. Parameters ---------- whereto: str """ if not self.is_installed(): raise RuntimeError( "cannot remember a state when a dataset is not yet installed") self.repo.checkout(whereto) def is_installed(self): """Returns whether a dataset is installed. A dataset is installed when a repository for it exists on the filesystem. Returns ------- bool """ return self.path is not None and exists(self.path) and \ self.repo is not None def get_superdataset(self, datalad_only=False, topmost=False, registered_only=True): """Get the dataset's superdataset Parameters ---------- datalad_only : bool, optional Whether to consider only "datalad datasets" (with non-None id), or (if False, which is default) - any git repository topmost : bool, optional Return the topmost super-dataset. Might then be the current one. registered_only : bool, optional Test whether any discovered superdataset actually contains the dataset in question as a registered subdataset (as opposed to just being located in a subdirectory without a formal relationship). Returns ------- Dataset or None """ from datalad.coreapi import subdatasets path = self.path sds_path = path if topmost else None def res_filter(res): return res.get('status') == 'ok' and res.get('type') == 'dataset' def subds_contains_path(ds, path): return path in sds.subdatasets(recursive=False, contains=path, result_filter=res_filter, on_failure='ignore', result_xfm='paths') while path: # normalize the path after adding .. so we guaranteed to not # follow into original directory if path itself is a symlink par_path = normpath(opj(path, pardir)) sds_path_ = get_dataset_root(par_path) if sds_path_ is None: # no more parents, use previous found break sds = Dataset(sds_path_) if datalad_only: # test if current git is actually a dataset? if not break if registered_only: if not subds_contains_path(sds, path): break # That was a good candidate sds_path = sds_path_ path = par_path if not topmost: # no looping break if sds_path is None: # None was found return None # No postprocessing now should be necessary since get_toppath # tries its best to not resolve symlinks now return Dataset(sds_path)
@optional_args def datasetmethod(f, name=None, dataset_argname='dataset'): """Decorator to bind functions to Dataset class. The decorated function is still directly callable and additionally serves as method `name` of class Dataset. To achieve this, the first positional argument is redirected to original keyword argument 'dataset_argname'. All other arguments stay in order (and keep their names, of course). That means, that the signature of the bound function is name(self, a, b) if the original signature is name(a, dataset, b) for example. The decorator has no effect on the actual function decorated with it. """ if not name: name = f.__name__ @wrapt.decorator def apply_func(wrapped, instance, args, kwargs): # Wrapper function to assign arguments of the bound function to # original function. # # Note # ---- # This wrapper is NOT returned by the decorator, but only used to bind # the function `f` to the Dataset class. kwargs = kwargs.copy() from datalad.utils import getargspec orig_pos = getargspec(f).args # If bound function is used with wrong signature (especially by # explicitly passing a dataset, let's raise a proper exception instead # of a 'list index out of range', that is not very telling to the user. if len(args) >= len(orig_pos): raise TypeError("{0}() takes at most {1} arguments ({2} given):" " {3}".format(name, len(orig_pos), len(args), ['self'] + [a for a in orig_pos if a != dataset_argname])) if dataset_argname in kwargs: raise TypeError("{}() got an unexpected keyword argument {}" "".format(name, dataset_argname)) kwargs[dataset_argname] = instance ds_index = orig_pos.index(dataset_argname) for i in range(0, len(args)): if i < ds_index: kwargs[orig_pos[i]] = args[i] elif i >= ds_index: kwargs[orig_pos[i+1]] = args[i] return f(**kwargs) setattr(Dataset, name, apply_func(f)) return f # Note: Cannot be defined within, since then needs to # be imported from, which needs to be imported from # for another constraint class EnsureDataset(Constraint): """Despite its name, this constraint does not actually ensure that the argument is a valid dataset, because for procedural reasons this would typically duplicate subsequent checks and processing. However, it can be used to achieve uniform documentation of `dataset` arguments.""" def __call__(self, value): if isinstance(value, Dataset): return value elif isinstance(value, str): # we cannot convert to a Dataset class right here # - duplicates require_dataset() later on # - we need to be able to distinguish between a bound # dataset method call and a standalone call for # relative path argument disambiguation #return Dataset(path=value) return value else: raise ValueError("Can't create Dataset from %s." % type(value)) def short_description(self): return "Dataset" def long_description(self): return """Value must be a Dataset or a valid identifier of a Dataset (e.g. a path)""" def require_dataset(dataset, check_installed=True, purpose=None): """Helper function to resolve a dataset. This function tries to resolve a dataset given an input argument, or based on the process' working directory, if `None` is given. Parameters ---------- dataset : None or path or Dataset Some value identifying a dataset or `None`. In the latter case a dataset will be searched based on the process working directory. check_installed : bool, optional If True, an optional check whether the resolved dataset is properly installed will be performed. purpose : str, optional This string will be inserted in error messages to make them more informative. The pattern is "... dataset for <STRING>". Returns ------- Dataset Or raises an exception (InsufficientArgumentsError). """ if dataset is not None and not isinstance(dataset, Dataset): dataset = Dataset(dataset) if dataset is None: # possible scenario of cmdline calls dspath = get_dataset_root(getpwd()) if not dspath: raise NoDatasetFound( "No dataset found at '{}'. Specify a dataset to work with " "by providing its path via the `dataset` option, " "or change the current working directory to be in a " "dataset.".format(getpwd())) dataset = Dataset(dspath) assert(dataset is not None) lgr.debug(u"Resolved dataset%s: %s", u' for {}'.format(purpose) if purpose else '', dataset.path) if check_installed and not dataset.is_installed(): raise ValueError(u"No installed dataset found at " u"{0}.".format(dataset.path)) return dataset # New helpers, courtesy of datalad-revolution. def resolve_path(path, ds=None): """Resolve a path specification (against a Dataset location) Any path is returned as an absolute path. If, and only if, a dataset object instance is given as `ds`, relative paths are interpreted as relative to the given dataset. In all other cases, relative paths are treated as relative to the current working directory. Note however, that this function is not able to resolve arbitrarily obfuscated path specifications. All operations are purely lexical, and no actual path resolution against the filesystem content is performed. Consequently, common relative path arguments like '../something' (relative to PWD) can be handled properly, but things like 'down/../under' cannot, as resolving this path properly depends on the actual target of any (potential) symlink leading up to '..'. Parameters ---------- path : str or PathLike or list Platform-specific path specific path specification. Multiple path specifications can be given as a list ds : Dataset or None Dataset instance to resolve relative paths against. Returns ------- `pathlib.Path` object or list(Path) When a list was given as input a list is returned, a Path instance otherwise. """ got_ds_instance = isinstance(ds, Dataset) if ds is not None and not got_ds_instance: ds = require_dataset( ds, check_installed=False, purpose='path resolution') out = [] for p in assure_list(path): if ds is None or not got_ds_instance: # no dataset at all or no instance provided -> CWD is always the reference # nothing needs to be done here. Path-conversion and absolutification # are done next pass # we have a given datasets instance elif not Path(p).is_absolute(): # we have a dataset and no abspath nor an explicit relative path -> # resolve it against the dataset p = ds.pathobj / p p = ut.Path(p) # make sure we return an absolute path, but without actually # resolving anything if not p.is_absolute(): # in general it is almost impossible to use resolve() when # we can have symlinks in the root path of a dataset # (that we don't want to resolve here), symlinks to annex'ed # files (that we never want to resolve), and other within-repo # symlinks that we (sometimes) want to resolve (i.e. symlinked # paths for addressing content vs adding content) # CONCEPT: do the minimal thing to catch most real-world inputs # ASSUMPTION: the only sane relative path input that needs # handling and can be handled are upward references like # '../../some/that', wherease stuff like 'down/../someotherdown' # are intellectual excercises # ALGORITHM: match any number of leading '..' path components # and shorten the PWD by that number # NOT using ut.Path.cwd(), because it has symlinks resolved!! pwd_parts = ut.Path(getpwd()).parts path_parts = leading_parents = 0 for pp in if pp == op.pardir: leading_parents += 1 path_parts = path_parts[1:] elif pp == op.curdir: # we want to discard that, but without stripping # a corresponding parent path_parts = path_parts[1:] else: break p = ut.Path( op.join( *(pwd_parts[:-leading_parents if leading_parents else None] + path_parts))) # note that we will not "normpath()" the result, check the # pathlib docs for why this is the only sane choice in the # face of the possibility of symlinks in the path out.append(p) return out[0] if isinstance(path, (str, PurePath)) else out # TODO keep this around for a while so that extensions can be updated rev_resolve_path = resolve_path def path_under_rev_dataset(ds, path): ds_path = ds.pathobj try: rpath = str(ut.Path(path).relative_to(ds_path)) if not rpath.startswith(op.pardir): # path is already underneath the dataset return path except Exception: # whatever went wrong, we gotta play save pass root = get_dataset_root(str(path)) while root is not None and not ds_path.samefile(root): # path and therefore root could be relative paths, # hence in the next round we cannot use dirname() # to jump in the the next directory up, but we have # to use ./.. and get_dataset_root() will handle # the rest just fine root = get_dataset_root(op.join(root, op.pardir)) if root is None: return None return ds_path / op.relpath(str(path), root) lgr.log(5, "Done importing dataset")