# pylint: disable=W0622,E1101
"""
A basic object-oriented interface for Galaxy entities.
"""
import bioblend
import abc, collections, httplib, json
__all__ = [
'Wrapper',
'Step',
'Workflow',
'ContentInfo',
'LibraryContentInfo',
'HistoryContentInfo',
'DatasetContainer',
'History',
'Library',
'Folder',
'Dataset',
'HistoryDatasetAssociation',
'LibraryDatasetDatasetAssociation',
'LibraryDataset',
'Tool',
'Preview',
'LibraryPreview',
'HistoryPreview',
'WorkflowPreview',
]
# sometimes the Galaxy API returns JSONs that contain other JSONs
def _recursive_loads(jdef):
try:
r = json.loads(jdef)
except (TypeError, ValueError):
r = jdef
if isinstance(r, collections.Sequence) and not isinstance(r, basestring):
for i, v in enumerate(r):
r[i] = _recursive_loads(v)
elif isinstance(r, collections.Mapping):
for k, v in r.iteritems():
r[k] = _recursive_loads(v)
return r
[docs]class Wrapper(object):
"""
Abstract base class for Galaxy entity wrappers.
Wrapper instances wrap deserialized JSON dictionaries such as the
ones obtained by the Galaxy web API, converting key-based access to
attribute-based access (e.g., ``library['name'] -> library.name``).
Dict keys that are converted to attributes are listed in the
``BASE_ATTRS`` class variable: this is the 'stable' interface.
Note that the wrapped dictionary is accessible via the ``wrapped``
attribute.
"""
BASE_ATTRS = ('id', 'name')
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def __init__(self, wrapped, parent=None, gi=None):
"""
:type wrapped: dict
:param wrapped: JSON-serializable dictionary
:type parent: :class:`Wrapper`
:param parent: the parent of this wrapper
:type gi: :class:`GalaxyInstance`
:param gi: the GalaxyInstance through which we can access this wrapper
"""
if not isinstance(wrapped, collections.Mapping):
raise TypeError('wrapped object must be a mapping type')
# loads(dumps(x)) is a bit faster than deepcopy and allows type checks
try:
dumped = json.dumps(wrapped)
except (TypeError, ValueError):
raise ValueError('wrapped object must be JSON-serializable')
object.__setattr__(self, 'wrapped', json.loads(dumped))
for k in self.BASE_ATTRS:
object.__setattr__(self, k, self.wrapped.get(k))
object.__setattr__(self, 'parent', parent)
object.__setattr__(self, 'is_modified', False)
object.__setattr__(self, 'gi', gi)
@abc.abstractproperty
def gi_module(self):
"""
The GalaxyInstance module that deals with objects of this type.
"""
pass
@property
def is_mapped(self):
"""
:obj:`True` if this wrapper is mapped to an actual Galaxy entity.
"""
return self.id is not None
[docs] def unmap(self):
"""
Disconnect this wrapper from Galaxy.
"""
object.__setattr__(self, 'id', None)
[docs] def clone(self):
"""
Return an independent copy of this wrapper.
"""
return self.__class__(self.wrapped)
[docs] def touch(self):
"""
Mark this wrapper as having been modified since its creation.
"""
object.__setattr__(self, 'is_modified', True)
if self.parent:
self.parent.touch()
[docs] def to_json(self):
"""
Return a JSON dump of this wrapper.
"""
return json.dumps(self.wrapped)
@classmethod
[docs] def from_json(cls, jdef):
"""
Build a new wrapper from a JSON dump.
"""
return cls(json.loads(jdef))
# FIXME: things like self.x[0] = 'y' do NOT call self.__setattr__
def __setattr__(self, name, value):
if name not in self.wrapped:
raise AttributeError("can't set attribute")
else:
self.wrapped[name] = value
object.__setattr__(self, name, value)
self.touch()
def __repr__(self):
return "%s(%r)" % (self.__class__.__name__, self.wrapped)
[docs]class Step(Wrapper):
"""
Abstract base class for workflow steps.
Steps are the main building blocks of a Galaxy workflow. A step
can refer to either an input dataset (type 'data_input`) or a
computational tool (type 'tool`).
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + (
'input_steps', 'tool_id', 'tool_inputs', 'tool_version', 'type'
)
def __init__(self, step_dict, parent):
super(Step, self).__init__(step_dict, parent=parent, gi=parent.gi)
if self.type == 'tool' and self.tool_inputs:
for k, v in self.tool_inputs.iteritems():
self.tool_inputs[k] = json.loads(v)
@property
def gi_module(self):
return self.gi.workflows
[docs]class Workflow(Wrapper):
"""
Workflows represent ordered sequences of computations on Galaxy.
A workflow defines a sequence of steps that produce one or more
results from an input dataset.
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + (
'deleted', 'inputs', 'published', 'steps', 'tags'
)
POLLING_INTERVAL = 10 # for output state monitoring
def __init__(self, wf_dict, gi=None):
super(Workflow, self).__init__(wf_dict, gi=gi)
missing_ids = []
for k, v in self.steps.iteritems():
# convert step ids to str for consistency with outer keys
v['id'] = str(v['id'])
for i in v['input_steps'].itervalues():
i['source_step'] = str(i['source_step'])
step = self._build_step(v, self)
self.steps[k] = step
if step.type == 'tool' and not step.tool_inputs:
missing_ids.append(k)
input_labels_to_ids = {}
for id_, d in self.inputs.iteritems():
input_labels_to_ids.setdefault(d['label'], set()).add(id_)
tool_labels_to_ids = {}
for s in self.steps.itervalues():
if s.type == 'tool':
tool_labels_to_ids.setdefault(s.tool_id, set()).add(s.id)
object.__setattr__(self, 'input_labels_to_ids', input_labels_to_ids)
object.__setattr__(self, 'tool_labels_to_ids', tool_labels_to_ids)
dag, inv_dag = self._get_dag()
heads, tails = set(dag), set(inv_dag)
object.__setattr__(self, '_dag', dag)
object.__setattr__(self, '_inv_dag', inv_dag)
object.__setattr__(self, 'input_ids', heads - tails)
assert self.input_ids == set(self.inputs)
object.__setattr__(self, 'output_ids', tails - heads)
object.__setattr__(self, 'missing_ids', missing_ids)
@property
def gi_module(self):
return self.gi.workflows
def _get_dag(self):
"""
Return the workflow's DAG.
For convenience, this method computes a 'direct' (step =>
successors) and an 'inverse' (step => predecessors)
representation of the same DAG.
For instance, a workflow with a single tool *c*, two inputs
*a, b* and three outputs *d, e, f* is represented by (direct)::
{'a': {'c'}, 'b': {'c'}, 'c': set(['d', 'e', 'f'])}
and by (inverse)::
{'c': set(['a', 'b']), 'd': {'c'}, 'e': {'c'}, 'f': {'c'}}
"""
dag, inv_dag = {}, {}
for s in self.steps.itervalues():
for i in s.input_steps.itervalues():
head, tail = i['source_step'], s.id
dag.setdefault(head, set()).add(tail)
inv_dag.setdefault(tail, set()).add(head)
return dag, inv_dag
@property
def dag(self):
return self._dag
@property
def inv_dag(self):
return self._inv_dag
[docs] def sorted_step_ids(self):
"""
Return a topological sort of the workflow's DAG.
"""
ids = []
input_ids = self.input_ids.copy()
inv_dag = dict((k, v.copy()) for k, v in self.inv_dag.iteritems())
while input_ids:
head = input_ids.pop()
ids.append(head)
for tail in self.dag.get(head, []):
incoming = inv_dag[tail]
incoming.remove(head)
if not incoming:
input_ids.add(tail)
return ids
@staticmethod
def _build_step(step_dict, parent):
try:
stype = step_dict['type']
except KeyError:
raise ValueError('not a step dict')
if stype not in set(['data_input', 'tool']):
raise ValueError('unknown step type: %r' % (stype,))
return Step(step_dict, parent)
@property
def data_input_ids(self):
"""
Return the list of data input steps for this workflow.
"""
return set(id_ for id_, s in self.steps.iteritems()
if s.type == 'data_input')
@property
def tool_ids(self):
"""
Return the list of tool steps for this workflow.
"""
return set(id_ for id_, s in self.steps.iteritems()
if s.type == 'tool')
@property
def input_labels(self):
"""
Return the labels of this workflow's input steps.
"""
return set(self.input_labels_to_ids)
@property
def is_runnable(self):
"""
Return True if the workflow can be run on Galaxy.
A workflow is considered runnable on a Galaxy instance if all
of the tools it uses are installed in that instance.
"""
return not self.missing_ids
[docs] def preview(self):
getf = self.gi.workflows.get_previews
try:
p = [_ for _ in getf(published=True) if _.id == self.id][0]
except IndexError:
raise ValueError('no object for id %s' % self.id)
return p
[docs] def run(self, input_map=None, history='', params=None, import_inputs=False,
replacement_params=None, wait=False,
polling_interval=POLLING_INTERVAL, break_on_error=True):
"""
Run the workflow in the current Galaxy instance.
:type input_map: dict
:param input_map: a mapping from workflow input labels to
datasets, e.g.: ``dict(zip(workflow.input_labels,
library.get_datasets()))``
:type history: :class:`History` or str
:param history: either a valid history object (results will be
stored there) or a string (a new history will be created with
the given name).
:type params: :class:`~collections.Mapping`
:param params: parameter settings for workflow steps (see below)
:type import_inputs: bool
:param import_inputs: If :obj:`True`, workflow inputs will be
imported into the history; if :obj:`False`, only workflow
outputs will be visible in the history.
:type replacement_params: :class:`~collections.Mapping`
:param replacement_params: pattern-based replacements for
post-job actions (see the docs for
:meth:`~bioblend.galaxy.workflows.WorkflowClient.run_workflow`)
:type wait: boolean
:param wait: whether to wait while the returned datasets are
in a pending state
:type polling_interval: float
:param polling_interval: polling interval in seconds
:type break_on_error: boolean
:param break_on_error: whether to break as soon as at least one
of the returned datasets is in the 'error' state
:rtype: tuple
:return: list of output datasets, output history
The ``params`` dict should be structured as follows::
PARAMS = {STEP_ID: PARAM_DICT, ...}
PARAM_DICT = {NAME: VALUE, ...}
For backwards compatibility, the following (deprecated) format is
also supported::
PARAMS = {TOOL_ID: PARAM_DICT, ...}
in which case PARAM_DICT affects all steps with the given tool id.
If both by-tool-id and by-step-id specifications are used, the
latter takes precedence.
Finally (again, for backwards compatibility), PARAM_DICT can also
be specified as::
PARAM_DICT = {'param': NAME, 'value': VALUE}
Note that this format allows only one parameter to be set per step.
Example: set 'a' to 1 for the third workflow step::
params = {workflow.steps[2].id: {'a': 1}}
.. warning::
This is a blocking operation that can take a very long time.
If ``wait`` is set to :obj:`False`, the method will return
as soon as the workflow has been *scheduled*, otherwise it
will wait until the workflow has been *run*. With a large
number of steps, however, the delay may not be negligible
even in the former case (e.g., minutes for 100 steps).
"""
if not self.is_mapped:
raise RuntimeError('workflow is not mapped to a Galaxy object')
if not self.is_runnable:
raise RuntimeError('workflow has missing tools: %s' % ', '.join(
'%s[%s]' % (self.steps[_].tool_id, _)
for _ in self.missing_ids
))
kwargs = {
'dataset_map': self.convert_input_map(input_map or {}),
'params': params,
'import_inputs_to_history': import_inputs,
'replacement_params': replacement_params,
}
if isinstance(history, History):
try:
kwargs['history_id'] = history.id
except AttributeError:
raise RuntimeError('history does not have an id')
elif isinstance(history, basestring):
kwargs['history_name'] = history
else:
raise TypeError(
'history must be either a history wrapper or a string'
)
res = self.gi.gi.workflows.run_workflow(self.id, **kwargs)
# res structure: {'history': HIST_ID, 'outputs': [DS_ID, DS_ID, ...]}
out_hist = self.gi.histories.get(res['history'])
assert set(res['outputs']).issubset(out_hist.dataset_ids)
outputs = [out_hist.get_dataset(_) for _ in res['outputs']]
if wait:
self.gi._wait_datasets(outputs, polling_interval=polling_interval,
break_on_error=break_on_error)
return outputs, out_hist
[docs] def export(self):
"""
Export a re-importable representation of the workflow.
:rtype: dict
:return: a JSON-serializable dump of the workflow
"""
return self.gi.gi.workflows.export_workflow_json(self.id)
[docs] def delete(self):
self.gi.workflows.delete(id_=self.id)
self.unmap()
[docs]class Dataset(Wrapper):
"""
Abstract base class for Galaxy datasets.
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + (
'data_type', 'file_name', 'file_size', 'state', 'deleted', 'file_ext'
)
__metaclass__ = abc.ABCMeta
POLLING_INTERVAL = 1 # for state monitoring
@abc.abstractmethod
def __init__(self, ds_dict, container, gi=None):
super(Dataset, self).__init__(ds_dict, gi=gi)
object.__setattr__(self, 'container', container)
@property
def container_id(self):
"""
Deprecated property.
Id of the dataset container. Use :attr:`.container.id` instead.
"""
return self.container.id
@abc.abstractproperty
def _stream_url(self):
pass
[docs] def get_stream(self, chunk_size=None):
"""
Open dataset for reading and return an iterator over its contents.
:type chunk_size: int
:param chunk_size: read this amount of bytes at a time
.. warning::
Due to a change in the Galaxy API endpoint, this method does
not work on :class:`LibraryDataset` instances with Galaxy
``release_2014.06.02`` and ``release_2014.08.11``. Methods
that delegate work to this one are also affected:
:meth:`peek`, :meth:`download`, :meth:`get_contents`.
"""
kwargs = {'stream': True}
if isinstance(self, LibraryDataset):
kwargs['params'] = {'ld_ids%5B%5D': self.id}
r = self.gi.gi.make_get_request(self._stream_url, **kwargs)
if isinstance(self, LibraryDataset) and r.status_code == 500:
# compatibility with older Galaxy releases
kwargs['params'] = {'ldda_ids%5B%5D': self.id}
r = self.gi.gi.make_get_request(self._stream_url, **kwargs)
r.raise_for_status()
return r.iter_content(chunk_size) # FIXME: client can't close r
[docs] def peek(self, chunk_size=None):
"""
Open dataset for reading and return the first chunk.
See :meth:`.get_stream` for param info.
"""
try:
return self.get_stream(chunk_size=chunk_size).next()
except StopIteration:
return ''
[docs] def download(self, file_object, chunk_size=None):
"""
Open dataset for reading and save its contents to ``file_object``.
:type outf: :obj:`file`
:param outf: output file object
See :meth:`.get_stream` for info on other params.
"""
for chunk in self.get_stream(chunk_size=chunk_size):
file_object.write(chunk)
[docs] def get_contents(self, chunk_size=None):
"""
Open dataset for reading and return its **full** contents.
See :meth:`.get_stream` for param info.
"""
return ''.join(self.get_stream(chunk_size=chunk_size))
[docs] def refresh(self):
"""
Re-fetch the attributes pertaining to this object.
Returns: self
"""
gi_client = getattr(self.gi.gi, self.container.API_MODULE)
ds_dict = gi_client.show_dataset(self.container.id, self.id)
self.__init__(ds_dict, self.container, self.gi)
return self
[docs] def wait(self, polling_interval=POLLING_INTERVAL, break_on_error=True):
self.gi._wait_datasets([self], polling_interval=polling_interval,
break_on_error=break_on_error)
[docs]class HistoryDatasetAssociation(Dataset):
"""
Maps to a Galaxy ``HistoryDatasetAssociation``.
"""
BASE_ATTRS = Dataset.BASE_ATTRS + ('tags', 'visible')
SRC = 'hda'
def __init__(self, ds_dict, container, gi=None):
super(HistoryDatasetAssociation, self).__init__(
ds_dict, container, gi=gi
)
@property
def gi_module(self):
return self.gi.histories
@property
def _stream_url(self):
base_url = self.gi.gi._make_url(
self.gi.gi.histories, module_id=self.container.id, contents=True
)
return "%s/%s/display" % (base_url, self.id)
[docs] def delete(self):
self.gi.gi.histories.delete_dataset(self.container.id, self.id)
self.container.refresh()
self.refresh()
class LibRelatedDataset(Dataset):
"""
"""
def __init__(self, ds_dict, container, gi=None):
super(LibRelatedDataset, self).__init__(ds_dict, container, gi=gi)
@property
def gi_module(self):
return self.gi.libraries
@property
def _stream_url(self):
base_url = self.gi.gi._make_url(self.gi.gi.libraries)
return "%s/datasets/download/uncompressed" % base_url
[docs]class LibraryDatasetDatasetAssociation(LibRelatedDataset):
"""
Maps to a Galaxy ``LibraryDatasetDatasetAssociation``.
"""
SRC = 'ldda'
[docs]class LibraryDataset(LibRelatedDataset):
"""
Maps to a Galaxy ``LibraryDataset``.
"""
SRC = 'ld'
[docs] def delete(self, purged=False):
self.gi.gi.libraries.delete_library_dataset(self.container.id, self.id, purged=purged)
self.container.refresh()
self.refresh()
[docs]class ContentInfo(Wrapper):
"""
Instances of this class wrap dictionaries obtained by getting
``/api/{histories,libraries}/<ID>/contents`` from Galaxy.
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + ('type',)
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def __init__(self, info_dict, gi=None):
super(ContentInfo, self).__init__(info_dict, gi=gi)
[docs]class LibraryContentInfo(ContentInfo):
"""
Instances of this class wrap dictionaries obtained by getting
``/api/libraries/<ID>/contents`` from Galaxy.
"""
def __init__(self, info_dict, gi=None):
super(LibraryContentInfo, self).__init__(info_dict, gi=gi)
if self.id.startswith('F'):
object.__setattr__(self, 'id', self.id[1:])
@property
def gi_module(self):
return self.gi.libraries
[docs]class HistoryContentInfo(ContentInfo):
"""
Instances of this class wrap dictionaries obtained by getting
``/api/histories/<ID>/contents`` from Galaxy.
"""
BASE_ATTRS = ContentInfo.BASE_ATTRS + ('deleted', 'state', 'visible')
def __init__(self, info_dict, gi=None):
super(HistoryContentInfo, self).__init__(info_dict, gi=gi)
@property
def gi_module(self):
return self.gi.histories
[docs]class DatasetContainer(Wrapper):
"""
Abstract base class for dataset containers (histories and libraries).
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + ('deleted',)
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def __init__(self, c_dict, content_infos=None, gi=None):
"""
:type content_infos: list of :class:`ContentInfo`
:param content_infos: info objects for the container's contents
"""
super(DatasetContainer, self).__init__(c_dict, gi=gi)
if content_infos is None:
content_infos = []
object.__setattr__(self, 'content_infos', content_infos)
@property
def dataset_ids(self):
return [_.id for _ in self.content_infos if _.type == 'file']
[docs] def preview(self):
getf = self.gi_module.get_previews
# self.state could be stale: check both regular and deleted containers
try:
p = [_ for _ in getf() if _.id == self.id][0]
except IndexError:
try:
p = [_ for _ in getf(deleted=True) if _.id == self.id][0]
except IndexError:
raise ValueError('no object for id %s' % self.id)
return p
[docs] def refresh(self):
"""
Re-fetch the attributes pertaining to this object.
Returns: self
"""
fresh = self.gi_module.get(self.id)
self.__init__(
fresh.wrapped, content_infos=fresh.content_infos, gi=self.gi
)
return self
[docs] def get_dataset(self, ds_id):
"""
Retrieve the dataset corresponding to the given id.
:type ds_id: str
:param ds_id: dataset id
:rtype: :class:`~.HistoryDatasetAssociation` or
:class:`~.LibraryDataset`
:return: the dataset corresponding to ``ds_id``
"""
gi_client = getattr(self.gi.gi, self.API_MODULE)
ds_dict = gi_client.show_dataset(self.id, ds_id)
return self.DS_TYPE(ds_dict, self, gi=self.gi)
[docs] def get_datasets(self, name=None):
"""
Get all datasets contained inside this dataset container.
:type name: str
:param name: return only datasets with this name
:rtype: list of :class:`~.HistoryDatasetAssociation` or list of
:class:`~.LibraryDataset`
:return: datasets with the given name contained inside this
container
.. note::
when filtering library datasets by name, specify their full
paths starting from the library's root folder, e.g.,
``/seqdata/reads.fastq``. Full paths are available through
the ``content_infos`` attribute of
:class:`~.Library` objects.
"""
if name is None:
ds_ids = self.dataset_ids
else:
ds_ids = [_.id for _ in self.content_infos if _.name == name]
return [self.get_dataset(_) for _ in ds_ids]
[docs]class History(DatasetContainer):
"""
Maps to a Galaxy history.
"""
BASE_ATTRS = DatasetContainer.BASE_ATTRS + ('annotation', 'state_ids', 'tags')
DS_TYPE = HistoryDatasetAssociation
CONTENT_INFO_TYPE = HistoryContentInfo
API_MODULE = 'histories'
def __init__(self, hist_dict, content_infos=None, gi=None):
super(History, self).__init__(
hist_dict, content_infos=content_infos, gi=gi
)
@property
def gi_module(self):
return self.gi.histories
[docs] def update(self, name=None, annotation=None, **kwds):
"""
Update history metadata information. Some of the attributes that can be
modified are documented below.
:type name: string
:param name: Replace history name with the given string
:type annotation: string
:param annotation: Replace history annotation with given string
:type deleted: boolean
:param deleted: Mark or unmark history as deleted
:type published: boolean
:param published: Mark or unmark history as published
:type importable: boolean
:param importable: Mark or unmark history as importable
:type tags: list
:param tags: Replace history tags with the given list
"""
# TODO: wouldn't it be better if name and annotation were attributes?
# TODO: do we need to ensure the attributes of `self` are the same as
# the ones returned by the call to `update_history` below?
res = self.gi.gi.histories.update_history(
self.id, name=name, annotation=annotation, **kwds
)
if res != httplib.OK:
raise RuntimeError('failed to update history')
self.refresh()
return self
[docs] def delete(self, purge=False):
self.gi.histories.delete(id_=self.id, purge=purge)
self.unmap()
[docs] def import_dataset(self, lds):
"""
Import a dataset into the history from a library.
:type lds: :class:`~.LibraryDataset`
:param lds: the library dataset to import
:rtype: :class:`~.HistoryDatasetAssociation`
:return: the imported history dataset
"""
if not self.is_mapped:
raise RuntimeError('history is not mapped to a Galaxy object')
if not isinstance(lds, LibraryDataset):
raise TypeError('lds is not a LibraryDataset')
res = self.gi.gi.histories.upload_dataset_from_library(self.id, lds.id)
if not isinstance(res, collections.Mapping):
raise RuntimeError(
'upload_dataset_from_library: unexpected reply: %r' % res
)
self.refresh()
return self.get_dataset(res['id'])
[docs] def upload_file(self, path, **kwargs):
"""
Upload the file specified by path to this history.
:type path: str
:param path: path of the file to upload
See :meth:`~bioblend.galaxy.tools.ToolClient.upload_file` for
the optional parameters.
:rtype: :class:`~.HistoryDatasetAssociation`
:return: the uploaded dataset
"""
out_dict = self.gi.gi.tools.upload_file(path, self.id, **kwargs)
self.refresh()
return self.get_dataset(out_dict['outputs'][0]['id'])
upload_dataset = upload_file
[docs] def paste_content(self, content, **kwargs):
"""
Upload a string to a new dataset in this history.
:type content: str
:param content: content of the new dataset to upload
See :meth:`~bioblend.galaxy.tools.ToolClient.upload_file` for
the optional parameters.
:rtype: :class:`~.HistoryDatasetAssociation`
:return: the uploaded dataset
"""
out_dict = self.gi.gi.tools.paste_content(content, self.id, **kwargs)
self.refresh()
return self.get_dataset(out_dict['outputs'][0]['id'])
[docs] def export(self, gzip=True, include_hidden=False, include_deleted=False,
wait=False):
"""
Start a job to create an export archive for this history. See
:meth:`~bioblend.galaxy.histories.HistoryClient.export_history`
for parameter and return value info.
"""
return self.gi.gi.histories.export_history(
self.id,
gzip=gzip,
include_hidden=include_hidden,
include_deleted=include_deleted,
wait=wait
)
[docs] def download(self, jeha_id, outf, chunk_size=bioblend.CHUNK_SIZE):
"""
Download an export archive for this history. Use :meth:`export`
to create an export and get the required ``jeha_id``. See
:meth:`~bioblend.galaxy.histories.HistoryClient.download_history`
for parameter and return value info.
"""
return self.gi.gi.histories.download_history(
self.id, jeha_id, outf, chunk_size=chunk_size
)
[docs]class Library(DatasetContainer):
"""
Maps to a Galaxy library.
"""
BASE_ATTRS = DatasetContainer.BASE_ATTRS + ('description', 'synopsis')
DS_TYPE = LibraryDataset
CONTENT_INFO_TYPE = LibraryContentInfo
API_MODULE = 'libraries'
def __init__(self, lib_dict, content_infos=None, gi=None):
super(Library, self).__init__(
lib_dict, content_infos=content_infos, gi=gi
)
@property
def gi_module(self):
return self.gi.libraries
@property
def folder_ids(self):
return [_.id for _ in self.content_infos if _.type == 'folder']
[docs] def delete(self):
self.gi.libraries.delete(id_=self.id)
self.unmap()
def __pre_upload(self, folder):
if not self.is_mapped:
raise RuntimeError('library is not mapped to a Galaxy object')
return None if folder is None else folder.id
[docs] def upload_data(self, data, folder=None, **kwargs):
"""
Upload data to this library.
:type data: str
:param data: dataset contents
:type folder: :class:`~.Folder`
:param folder: a folder object, or :obj:`None` to upload to
the root folder
:rtype: :class:`~.LibraryDataset`
:return: the dataset object that represents the uploaded content
Optional keyword arguments: ``file_type``, ``dbkey``.
"""
fid = self.__pre_upload(folder)
res = self.gi.gi.libraries.upload_file_contents(
self.id, data, folder_id=fid, **kwargs
)
self.refresh()
return self.get_dataset(res[0]['id'])
[docs] def upload_from_url(self, url, folder=None, **kwargs):
"""
Upload data to this library from the given URL.
:type url: str
:param url: URL from which data should be read
See :meth:`.upload_data` for info on other params.
"""
fid = self.__pre_upload(folder)
res = self.gi.gi.libraries.upload_file_from_url(
self.id, url, folder_id=fid, **kwargs
)
self.refresh()
return self.get_dataset(res[0]['id'])
[docs] def upload_from_local(self, path, folder=None, **kwargs):
"""
Upload data to this library from a local file.
:type path: str
:param path: local file path from which data should be read
See :meth:`.upload_data` for info on other params.
"""
fid = self.__pre_upload(folder)
res = self.gi.gi.libraries.upload_file_from_local_path(
self.id, path, folder_id=fid, **kwargs
)
self.refresh()
return self.get_dataset(res[0]['id'])
[docs] def upload_from_galaxy_fs(self, paths, folder=None, link_data_only=None, **kwargs):
"""
Upload data to this library from filesystem paths on the server.
.. note::
For this method to work, the Galaxy instance must have the
``allow_library_path_paste`` option set to ``True`` in the
``config/galaxy.ini`` configuration file.
:type paths: str or :class:`~collections.Iterable` of str
:param paths: server-side file paths from which data should be read
:type link_data_only: str
:param link_data_only: either 'copy_files' (default) or
'link_to_files'. Setting to 'link_to_files' symlinks instead of
copying the files
:rtype: list of :class:`~.LibraryDataset`
:return: the dataset objects that represent the uploaded content
See :meth:`.upload_data` for info on other params.
"""
fid = self.__pre_upload(folder)
if isinstance(paths, basestring):
paths = (paths,)
paths = '\n'.join(paths)
res = self.gi.gi.libraries.upload_from_galaxy_filesystem(
self.id, paths, folder_id=fid, link_data_only=link_data_only,
**kwargs
)
if res is None:
raise RuntimeError('upload_from_galaxy_filesystem: no reply')
if not isinstance(res, collections.Sequence):
raise RuntimeError(
'upload_from_galaxy_filesystem: unexpected reply: %r' % res
)
new_datasets = [
self.get_dataset(ds_info['id']) for ds_info in res
]
self.refresh()
return new_datasets
[docs] def copy_from_dataset(self, hda, folder=None, message=''):
"""
Copy a history dataset into this library.
:type hda: :class:`~.HistoryDatasetAssociation`
:param hda: history dataset to copy into the library
See :meth:`.upload_data` for info on other params.
"""
fid = self.__pre_upload(folder)
res = self.gi.gi.libraries.copy_from_dataset(
self.id, hda.id, folder_id=fid, message=message
)
self.refresh()
return self.get_dataset(res['library_dataset_id'])
[docs] def create_folder(self, name, description=None, base_folder=None):
"""
Create a folder in this library.
:type name: str
:param name: folder name
:type description: str
:param description: optional folder description
:type base_folder: :class:`~.Folder`
:param base_folder: parent folder, or :obj:`None` to create in
the root folder
:rtype: :class:`~.Folder`
:return: the folder just created
"""
bfid = None if base_folder is None else base_folder.id
res = self.gi.gi.libraries.create_folder(
self.id, name, description=description, base_folder_id=bfid,
)
self.refresh()
return self.get_folder(res[0]['id'])
[docs] def get_folder(self, f_id):
"""
Retrieve the folder corresponding to the given id.
:rtype: :class:`~.Folder`
:return: the folder corresponding to ``f_id``
"""
f_dict = self.gi.gi.libraries.show_folder(self.id, f_id)
return Folder(f_dict, self.id, gi=self.gi)
[docs]class Folder(Wrapper):
"""
Maps to a folder in a Galaxy library.
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + ('description', 'item_count')
def __init__(self, f_dict, container_id, gi=None):
super(Folder, self).__init__(f_dict, gi=gi)
if self.id.startswith('F'): # folder id from library contents
object.__setattr__(self, 'id', self.id[1:])
object.__setattr__(self, 'container_id', container_id)
@property
def gi_module(self):
return self.gi.libraries
[docs]class Preview(Wrapper):
"""
Abstract base class for Galaxy entity 'previews'.
Classes derived from this one model the short summaries returned
by global getters such as ``/api/libraries``.
"""
BASE_ATTRS = Wrapper.BASE_ATTRS + ('deleted',)
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def __init__(self, pw_dict, gi=None):
super(Preview, self).__init__(pw_dict, gi=gi)
[docs]class LibraryPreview(Preview):
"""
Models Galaxy library 'previews'.
Instances of this class wrap dictionaries obtained by getting
``/api/libraries`` from Galaxy.
"""
def __init__(self, pw_dict, gi=None):
super(LibraryPreview, self).__init__(pw_dict, gi=gi)
@property
def gi_module(self):
return self.gi.libraries
[docs]class HistoryPreview(Preview):
"""
Models Galaxy history 'previews'.
Instances of this class wrap dictionaries obtained by getting
``/api/histories`` from Galaxy.
"""
BASE_ATTRS = Preview.BASE_ATTRS + ('tags',)
def __init__(self, pw_dict, gi=None):
super(HistoryPreview, self).__init__(pw_dict, gi=gi)
@property
def gi_module(self):
return self.gi.histories
[docs]class WorkflowPreview(Preview):
"""
Models Galaxy workflow 'previews'.
Instances of this class wrap dictionaries obtained by getting
``/api/workflows`` from Galaxy.
"""
BASE_ATTRS = Preview.BASE_ATTRS + ('published', 'tags')
def __init__(self, pw_dict, gi=None):
super(WorkflowPreview, self).__init__(pw_dict, gi=gi)
@property
def gi_module(self):
return self.gi.workflows