Warning
This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
Source code for galaxy.managers.collections
import logging
from typing import (
Any,
Dict,
List,
overload,
Union,
)
from zipfile import ZipFile
from sqlalchemy.orm import (
joinedload,
Query,
)
from typing_extensions import Literal
from galaxy import model
from galaxy.datatypes.registry import Registry
from galaxy.exceptions import (
ItemAccessibilityException,
MessageException,
RequestParameterInvalidException,
)
from galaxy.managers.collections_util import validate_input_element_identifiers
from galaxy.managers.context import (
ProvidesAppContext,
ProvidesHistoryContext,
ProvidesUserContext,
)
from galaxy.managers.hdas import (
HDAManager,
HistoryDatasetAssociationNoHistoryException,
)
from galaxy.managers.hdcas import write_dataset_collection
from galaxy.managers.histories import HistoryManager
from galaxy.managers.lddas import LDDAManager
from galaxy.model.base import transaction
from galaxy.model.dataset_collections import builder
from galaxy.model.dataset_collections.matching import MatchingCollections
from galaxy.model.dataset_collections.registry import DATASET_COLLECTION_TYPES_REGISTRY
from galaxy.model.dataset_collections.type_description import COLLECTION_TYPE_DESCRIPTION_FACTORY
from galaxy.model.mapping import GalaxyModelMapping
from galaxy.schema.schema import DatasetCollectionInstanceType
from galaxy.schema.tasks import PrepareDatasetCollectionDownload
from galaxy.security.idencoding import IdEncodingHelper
from galaxy.short_term_storage import (
ShortTermStorageMonitor,
storage_context,
)
from galaxy.util import validation
log = logging.getLogger(__name__)
ERROR_INVALID_ELEMENTS_SPECIFICATION = "Create called with invalid parameters, must specify element identifiers."
ERROR_NO_COLLECTION_TYPE = "Create called without specifying a collection type."
[docs]class DatasetCollectionManager:
"""
Abstraction for interfacing with dataset collections instance - ideally abstracts
out model and plugin details.
"""
ELEMENTS_UNINITIALIZED = object()
[docs] def __init__(
self,
model: GalaxyModelMapping,
security: IdEncodingHelper,
hda_manager: HDAManager,
history_manager: HistoryManager,
ldda_manager: LDDAManager,
short_term_storage_monitor: ShortTermStorageMonitor,
):
self.type_registry = DATASET_COLLECTION_TYPES_REGISTRY
self.collection_type_descriptions = COLLECTION_TYPE_DESCRIPTION_FACTORY
self.model = model
self.security = security
self.short_term_storage_monitor = short_term_storage_monitor
self.hda_manager = hda_manager
self.history_manager = history_manager
self.ldda_manager = ldda_manager
[docs] def precreate_dataset_collection_instance(
self,
trans: ProvidesHistoryContext,
parent,
name,
structure,
implicit_inputs=None,
implicit_output_name=None,
tags=None,
completed_collection=None,
):
# TODO: prebuild all required HIDs and send them in so no need to flush in between.
dataset_collection = self.precreate_dataset_collection(
structure,
allow_uninitialized_element=implicit_output_name is not None,
completed_collection=completed_collection,
implicit_output_name=implicit_output_name,
)
instance = self._create_instance_for_collection(
trans,
parent,
name,
dataset_collection,
implicit_inputs=implicit_inputs,
implicit_output_name=implicit_output_name,
flush=False,
tags=tags,
)
return instance
[docs] def precreate_dataset_collection(
self, structure, allow_uninitialized_element=True, completed_collection=None, implicit_output_name=None
):
has_structure = not structure.is_leaf and structure.children_known
if not has_structure and allow_uninitialized_element:
dataset_collection = model.DatasetCollectionElement.UNINITIALIZED_ELEMENT
elif not has_structure:
collection_type_description = structure.collection_type_description
dataset_collection = model.DatasetCollection(populated=False)
dataset_collection.collection_type = collection_type_description.collection_type
else:
collection_type_description = structure.collection_type_description
dataset_collection = model.DatasetCollection(populated=False)
dataset_collection.collection_type = collection_type_description.collection_type
elements = []
for index, (identifier, substructure) in enumerate(structure.children):
# TODO: Open question - populate these now or later?
element = None
if completed_collection and implicit_output_name:
job = completed_collection[index]
if job:
it = (
jtiodca.dataset_collection
for jtiodca in job.output_dataset_collections
if jtiodca.name == implicit_output_name
)
element = next(it, None)
if element is None:
if substructure.is_leaf:
element = model.DatasetCollectionElement.UNINITIALIZED_ELEMENT
else:
element = self.precreate_dataset_collection(
substructure, allow_uninitialized_element=allow_uninitialized_element
)
element = model.DatasetCollectionElement(
collection=dataset_collection,
element=element,
element_identifier=identifier,
element_index=index,
)
elements.append(element)
dataset_collection.element_count = len(elements)
return dataset_collection
[docs] def create(
self,
trans: ProvidesHistoryContext,
parent,
name,
collection_type,
element_identifiers=None,
elements=None,
implicit_collection_info=None,
trusted_identifiers=None,
hide_source_items=False,
tags=None,
copy_elements=False,
history=None,
set_hid=True,
flush=True,
completed_job=None,
output_name=None,
):
"""
PRECONDITION: security checks on ability to add to parent
occurred during load.
"""
# Trust embedded, newly created objects created by tool subsystem.
if trusted_identifiers is None:
trusted_identifiers = implicit_collection_info is not None
if element_identifiers and not trusted_identifiers:
validate_input_element_identifiers(element_identifiers)
if completed_job and output_name:
jtodca = next(a for a in completed_job.output_dataset_collection_instances if a.name == output_name)
dataset_collection = jtodca.dataset_collection_instance.collection
else:
dataset_collection = self.create_dataset_collection(
trans=trans,
collection_type=collection_type,
element_identifiers=element_identifiers,
elements=elements,
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
implicit_inputs = []
if implicit_collection_info:
implicit_inputs = implicit_collection_info.get("implicit_inputs", [])
implicit_output_name = None
if implicit_collection_info:
implicit_output_name = implicit_collection_info["implicit_output_name"]
return self._create_instance_for_collection(
trans,
parent,
name,
dataset_collection,
implicit_inputs=implicit_inputs,
implicit_output_name=implicit_output_name,
tags=tags,
set_hid=set_hid,
flush=flush,
)
def _create_instance_for_collection(
self,
trans: ProvidesHistoryContext,
parent,
name,
dataset_collection,
implicit_output_name=None,
implicit_inputs=None,
tags=None,
set_hid=True,
flush=True,
):
if isinstance(parent, model.History):
dataset_collection_instance: Union[
model.HistoryDatasetCollectionAssociation,
model.LibraryDatasetCollectionAssociation,
] = model.HistoryDatasetCollectionAssociation(
collection=dataset_collection,
name=name,
)
assert isinstance(dataset_collection_instance, model.HistoryDatasetCollectionAssociation)
if implicit_inputs:
for input_name, input_collection in implicit_inputs:
dataset_collection_instance.add_implicit_input_collection(input_name, input_collection)
if implicit_output_name:
dataset_collection_instance.implicit_output_name = implicit_output_name
log.debug("Created collection with %d elements" % (len(dataset_collection_instance.collection.elements)))
if set_hid:
parent.add_dataset_collection(dataset_collection_instance)
elif isinstance(parent, model.LibraryFolder):
dataset_collection_instance = model.LibraryDatasetCollectionAssociation(
collection=dataset_collection,
folder=parent,
name=name,
)
else:
message = f"Internal logic error - create called with unknown parent type {type(parent)}"
log.exception(message)
raise MessageException(message)
# Tags may be coming in as a dictionary of tag model objects if copying them from other
# existing Galaxy objects or as a list of strings if the tags are coming from user supplied
# values.
if isinstance(tags, list):
assert implicit_inputs is None, implicit_inputs
tags = trans.tag_handler.add_tags_from_list(trans.user, dataset_collection_instance, tags, flush=False)
else:
tags = self._append_tags(dataset_collection_instance, implicit_inputs, tags)
return self.__persist(dataset_collection_instance, flush=flush)
[docs] def create_dataset_collection(
self,
trans: ProvidesHistoryContext,
collection_type,
element_identifiers=None,
elements=None,
hide_source_items=None,
copy_elements=False,
history=None,
):
# Make sure at least one of these is None.
assert element_identifiers is None or elements is None
if element_identifiers is None and elements is None:
raise RequestParameterInvalidException(ERROR_INVALID_ELEMENTS_SPECIFICATION)
if not collection_type:
raise RequestParameterInvalidException(ERROR_NO_COLLECTION_TYPE)
collection_type_description = self.collection_type_descriptions.for_collection_type(collection_type)
has_subcollections = collection_type_description.has_subcollections()
# If we have elements, this is an internal request, don't need to load
# objects from identifiers.
if elements is None:
elements = self._element_identifiers_to_elements(
trans,
collection_type_description=collection_type_description,
element_identifiers=element_identifiers,
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
if history:
history.add_pending_items()
else:
if has_subcollections:
# Nested collection - recursively create collections as needed.
self.__recursively_create_collections_for_elements(
trans, elements, hide_source_items, copy_elements=copy_elements, history=history
)
# else if elements is set, it better be an ordered dict!
if elements is not self.ELEMENTS_UNINITIALIZED:
type_plugin = collection_type_description.rank_type_plugin()
dataset_collection = builder.build_collection(type_plugin, elements)
else:
dataset_collection = model.DatasetCollection(populated=False)
dataset_collection.collection_type = collection_type
return dataset_collection
[docs] def get_converters_for_collection(
self, trans: ProvidesHistoryContext, id, datatypes_registry: Registry, instance_type="history"
):
dataset_collection_instance = self.get_dataset_collection_instance(
trans, id=id, instance_type=instance_type, check_ownership=True
)
dbkeys_and_extensions = dataset_collection_instance.dataset_dbkeys_and_extensions_summary
suitable_converters = set()
first_extension = True
most_recent_datatype = None
# TODO error checking
for datatype in dbkeys_and_extensions[1]:
new_converters = datatypes_registry.get_converters_by_datatype(datatype)
set_of_new_converters = set()
for tgt_type, tgt_val in new_converters.items():
converter = (tgt_type, tgt_val)
set_of_new_converters.add(converter)
if first_extension is True:
suitable_converters = set_of_new_converters
most_recent_datatype = datatype
first_extension = False
else:
suitable_converters = suitable_converters.intersection(set_of_new_converters)
if suitable_converters:
most_recent_datatype = datatype
suitable_tool_ids = []
for tool in suitable_converters:
tool_info = {
"tool_id": tool[1].id,
"name": tool[1].name,
"target_type": tool[0],
"original_type": most_recent_datatype,
}
suitable_tool_ids.append(tool_info)
return suitable_tool_ids
def _element_identifiers_to_elements(
self,
trans: ProvidesHistoryContext,
collection_type_description,
element_identifiers,
hide_source_items=False,
copy_elements=False,
history=None,
):
if collection_type_description.has_subcollections():
# Nested collection - recursively create collections and update identifiers.
self.__recursively_create_collections_for_identifiers(
trans, element_identifiers, hide_source_items, copy_elements, history=history
)
new_collection = False
for element_identifier in element_identifiers:
if element_identifier.get("src") == "new_collection" and element_identifier.get("collection_type") == "":
new_collection = True
elements = self.__load_elements(
trans=trans,
element_identifiers=element_identifier["element_identifiers"],
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
if not new_collection:
elements = self.__load_elements(
trans=trans,
element_identifiers=element_identifiers,
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
return elements
def _append_tags(self, dataset_collection_instance, implicit_inputs=None, tags=None):
tags = tags or {}
implicit_inputs = implicit_inputs or []
for _, v in implicit_inputs:
for tag in v.auto_propagated_tags:
tags[tag.value] = tag
for _, tag in tags.items():
dataset_collection_instance.tags.append(tag.copy(cls=model.HistoryDatasetCollectionTagAssociation))
[docs] def collection_builder_for(self, dataset_collection):
return builder.BoundCollectionBuilder(dataset_collection)
[docs] def delete(self, trans: ProvidesHistoryContext, instance_type, id, recursive=False, purge=False):
dataset_collection_instance = self.get_dataset_collection_instance(
trans, instance_type, id, check_ownership=True
)
dataset_collection_instance.deleted = True
trans.sa_session.add(dataset_collection_instance)
async_result = None
if recursive:
for dataset in dataset_collection_instance.collection.dataset_instances:
try:
self.hda_manager.error_unless_owner(dataset, user=trans.get_user(), current_history=trans.history)
except HistoryDatasetAssociationNoHistoryException:
log.info(
f"Cannot delete HistoryDatasetAssociation {dataset.id}, HistoryDatasetAssociation has no associated History, cannot verify owner"
)
continue
if not dataset.deleted:
dataset.deleted = True
if purge and not dataset.purged:
async_result = self.hda_manager.purge(dataset, user=trans.user)
with transaction(trans.sa_session):
trans.sa_session.commit()
return async_result
[docs] def update(self, trans: ProvidesHistoryContext, instance_type, id, payload):
dataset_collection_instance = self.get_dataset_collection_instance(
trans, instance_type, id, check_ownership=True
)
if trans.user is None:
anon_allowed_payload = {}
if "deleted" in payload:
anon_allowed_payload["deleted"] = payload["deleted"]
if "visible" in payload:
anon_allowed_payload["visible"] = payload["visible"]
payload = self._validate_and_parse_update_payload(anon_allowed_payload)
else:
payload = self._validate_and_parse_update_payload(payload)
changed = self._set_from_dict(trans, dataset_collection_instance, payload)
return changed
[docs] def copy(
self,
trans: ProvidesHistoryContext,
parent,
source,
encoded_source_id,
copy_elements=False,
dataset_instance_attributes=None,
):
"""
PRECONDITION: security checks on ability to add to parent occurred
during load.
"""
assert source == "hdca" # for now
source_hdca = self.__get_history_collection_instance(trans, encoded_source_id)
copy_kwds = {}
if copy_elements:
copy_kwds["element_destination"] = parent # e.g. a history
if dataset_instance_attributes is not None:
copy_kwds["dataset_instance_attributes"] = dataset_instance_attributes
new_hdca = source_hdca.copy(flush=False, **copy_kwds)
new_hdca.copy_tags_from(target_user=trans.get_user(), source=source_hdca)
if not copy_elements:
parent.add_dataset_collection(new_hdca)
with transaction(trans.sa_session):
trans.sa_session.commit()
return new_hdca
def _set_from_dict(self, trans: ProvidesUserContext, dataset_collection_instance, new_data):
# send what we can down into the model
changed = dataset_collection_instance.set_from_dict(new_data)
# the rest (often involving the trans) - do here
if "annotation" in new_data.keys() and trans.get_user():
dataset_collection_instance.add_item_annotation(
trans.sa_session, trans.get_user(), dataset_collection_instance, new_data["annotation"]
)
changed["annotation"] = new_data["annotation"]
# the api promises a list of changed fields, but tags are not marked as changed to avoid the
# flush, so we must handle changed tag responses manually
new_tags = None
if "tags" in new_data.keys():
# set_tags_from_list will flush on its own, no need to add to 'changed' here and incur a second flush.
new_tags = trans.tag_handler.set_tags_from_list(
trans.user,
dataset_collection_instance,
new_data["tags"],
)
if changed.keys():
with transaction(trans.sa_session):
trans.sa_session.commit()
# set client tag field response after the flush
if new_tags is not None:
changed["tags"] = dataset_collection_instance.make_tag_string_list()
return changed
def _validate_and_parse_update_payload(self, payload):
validated_payload = {}
for key, val in payload.items():
if val is None:
continue
if key in ("name"):
val = validation.validate_and_sanitize_basestring(key, val)
validated_payload[key] = val
if key in ("deleted", "visible"):
validated_payload[key] = validation.validate_boolean(key, val)
elif key == "tags":
validated_payload[key] = validation.validate_and_sanitize_basestring_list(key, val)
return validated_payload
[docs] def history_dataset_collections(self, history, query):
collections = history.active_dataset_collections
collections = list(filter(query.direct_match, collections))
return collections
def __persist(self, dataset_collection_instance, flush=True):
context = self.model.context
context.add(dataset_collection_instance)
if flush:
with transaction(context):
context.commit()
return dataset_collection_instance
def __recursively_create_collections_for_identifiers(
self, trans, element_identifiers, hide_source_items, copy_elements, history=None
):
for element_identifier in element_identifiers:
try:
if element_identifier.get("src") != "new_collection":
# not a new collection, keep moving...
continue
except KeyError:
# Not a dictionary, just an id of an HDA - move along.
continue
# element identifier is a dict with src new_collection...
collection_type = element_identifier.get("collection_type")
collection = self.create_dataset_collection(
trans=trans,
collection_type=collection_type,
element_identifiers=element_identifier["element_identifiers"],
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
element_identifier["__object__"] = collection
return element_identifiers
def __recursively_create_collections_for_elements(
self, trans, elements, hide_source_items, copy_elements, history=None
):
if elements is self.ELEMENTS_UNINITIALIZED:
return
new_elements = {}
for key, element in elements.items():
if isinstance(element, model.DatasetCollection):
continue
if element.get("src") != "new_collection":
continue
# element is a dict with src new_collection and
# and dict of named elements
collection_type = element.get("collection_type")
sub_elements = element["elements"]
collection = self.create_dataset_collection(
trans=trans,
collection_type=collection_type,
elements=sub_elements,
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
new_elements[key] = collection
elements.update(new_elements)
def __load_elements(self, trans, element_identifiers, hide_source_items=False, copy_elements=False, history=None):
elements = {}
for element_identifier in element_identifiers:
elements[element_identifier["name"]] = self.__load_element(
trans,
element_identifier=element_identifier,
hide_source_items=hide_source_items,
copy_elements=copy_elements,
history=history,
)
return elements
def __load_element(self, trans, element_identifier, hide_source_items, copy_elements, history=None):
# if not isinstance( element_identifier, dict ):
# # Is allowing this to just be the id of an hda too clever? Somewhat
# # consistent with other API methods though.
# element_identifier = dict( src='hda', id=str( element_identifier ) )
# Previously created collection already found in request, just pass
# through as is.
if "__object__" in element_identifier:
the_object = element_identifier["__object__"]
if the_object is not None and the_object.id:
context = self.model.context
if the_object not in context:
the_object = context.get(type(the_object), the_object.id)
return the_object
# dataset_identifier is dict {src=hda|ldda|hdca|new_collection, id=<encoded_id>}
try:
src_type = element_identifier.get("src", "hda")
except AttributeError:
raise MessageException(f"Dataset collection element definition ({element_identifier}) not dictionary-like.")
element_id = element_identifier.get("id")
if not src_type or not element_id:
message_template = "Problem decoding element identifier %s - must contain a 'src' and a 'id'."
message = message_template % element_identifier
raise RequestParameterInvalidException(message)
tag_str = ""
if tags := element_identifier.pop("tags", None):
tag_str = ",".join(str(_) for _ in tags)
if src_type == "hda":
hda = self.hda_manager.get_accessible(element_id, trans.user)
if copy_elements:
element: model.HistoryDatasetAssociation = self.hda_manager.copy(
hda, history=history or trans.history, hide_copy=True, flush=False
)
else:
element = hda
if hide_source_items and self.hda_manager.get_owned(
hda.id, user=trans.user, current_history=history or trans.history
):
hda.visible = False
trans.tag_handler.apply_item_tags(user=trans.user, item=element, tags_str=tag_str, flush=False)
return element
elif src_type == "ldda":
element2 = self.ldda_manager.get(trans, element_id, check_accessible=True)
element3 = element2.to_history_dataset_association(
history or trans.history, add_to_history=True, visible=not hide_source_items
)
trans.tag_handler.apply_item_tags(user=trans.user, item=element3, tags_str=tag_str, flush=False)
return element3
elif src_type == "hdca":
# TODO: Option to copy? Force copy? Copy or allow if not owned?
return self.__get_history_collection_instance(trans, element_id).collection
# TODO: ldca.
raise RequestParameterInvalidException(f"Unknown src_type parameter supplied '{src_type}'.")
[docs] def match_collections(self, collections_to_match):
"""
May seem odd to place it here, but planning to grow sophistication and
get plugin types involved so it will likely make sense in the future.
"""
return MatchingCollections.for_collections(collections_to_match, self.collection_type_descriptions)
@overload
def get_dataset_collection_instance(
self, trans: ProvidesHistoryContext, instance_type: Literal["history"], id, **kwds: Any
) -> model.HistoryDatasetCollectionAssociation: ...
@overload
def get_dataset_collection_instance(
self, trans: ProvidesHistoryContext, instance_type: Literal["library"], id, **kwds: Any
) -> model.LibraryDatasetCollectionAssociation: ...
[docs] def get_dataset_collection_instance(
self, trans: ProvidesHistoryContext, instance_type: DatasetCollectionInstanceType, id, **kwds: Any
) -> Union[model.HistoryDatasetCollectionAssociation, model.LibraryDatasetCollectionAssociation]:
""" """
if instance_type == "history":
return self.__get_history_collection_instance(trans, id, **kwds)
elif instance_type == "library":
return self.__get_library_collection_instance(trans, id, **kwds)
raise NotImplementedError()
[docs] def get_dataset_collection(self, trans, encoded_id):
collection_id = int(trans.app.security.decode_id(encoded_id))
collection = trans.sa_session.get(trans.app.model.DatasetCollection, collection_id)
return collection
[docs] def apply_rules(self, hdca, rule_set, handle_dataset):
hdca_collection = hdca.collection
collection_type = hdca_collection.collection_type
elements = hdca_collection.elements
collection_type_description = self.collection_type_descriptions.for_collection_type(collection_type)
initial_data, initial_sources = self.__init_rule_data(elements, collection_type_description)
data, sources = rule_set.apply(initial_data, initial_sources)
collection_type = rule_set.collection_type
collection_type_description = self.collection_type_descriptions.for_collection_type(collection_type)
elements = self._build_elements_from_rule_data(
collection_type_description, rule_set, data, sources, handle_dataset
)
return elements
def _build_elements_from_rule_data(self, collection_type_description, rule_set, data, sources, handle_dataset):
identifier_columns = rule_set.identifier_columns
mapping_as_dict = rule_set.mapping_as_dict
elements: Dict[str, Any] = {}
for data_index, row_data in enumerate(data):
# For each row, find place in depth for this element.
collection_type_at_depth = collection_type_description
elements_at_depth = elements
for i, identifier_column in enumerate(identifier_columns):
identifier = row_data[identifier_column]
if i + 1 == len(identifier_columns):
# At correct final position in nested structure for this dataset.
if collection_type_at_depth.collection_type == "paired":
if identifier.lower() in ["f", "1", "r1", "forward"]:
identifier = "forward"
elif identifier.lower() in ["r", "2", "r2", "reverse"]:
identifier = "reverse"
else:
raise Exception(
"Unknown indicator of paired status encountered - only values of F, R, 1, 2, R1, R2, forward, or reverse are allowed."
)
tags = []
if "group_tags" in mapping_as_dict:
columns = mapping_as_dict["group_tags"]["columns"]
for tag_column in columns:
tag = row_data[tag_column]
tags.append(f"group:{tag}")
if "tags" in mapping_as_dict:
columns = mapping_as_dict["tags"]["columns"]
for tag_column in columns:
tag = row_data[tag_column]
tags.append(tag)
effective_dataset = handle_dataset(sources[data_index]["dataset"], tags)
elements_at_depth[identifier] = effective_dataset
# log.info("Handling dataset [%s] with sources [%s], need to add tags [%s]" % (effective_dataset, sources, tags))
else:
collection_type_at_depth = collection_type_at_depth.child_collection_type_description()
found = False
if identifier in elements_at_depth:
elements_at_depth = elements_at_depth[identifier]["elements"]
found = True
if not found:
# Create a new collection whose elements are defined in the next loop
sub_collection: Dict[str, Any] = {}
sub_collection["src"] = "new_collection"
sub_collection["collection_type"] = collection_type_at_depth.collection_type
sub_collection["elements"] = {}
# Update elements with new collection
elements_at_depth[identifier] = sub_collection
# Subsequent loop fills elements of newly created collection
elements_at_depth = sub_collection["elements"]
return elements
def __init_rule_data(self, elements, collection_type_description, parent_identifiers=None):
parent_identifiers = parent_identifiers or []
data: List[List[str]] = []
sources: List[Dict[str, str]] = []
for element in elements:
element_object = element.element_object
identifiers = parent_identifiers + [element.element_identifier]
if not element.is_collection:
data.append([])
source = {
"identifiers": identifiers,
"dataset": element_object,
"tags": element_object.make_tag_string_list(),
}
sources.append(source)
else:
child_collection_type_description = collection_type_description.child_collection_type_description()
element_data, element_sources = self.__init_rule_data(
element_object.elements, child_collection_type_description, identifiers
)
data.extend(element_data)
sources.extend(element_sources)
return data, sources
def __get_history_collection_instance(
self, trans: ProvidesHistoryContext, id, check_ownership=False, check_accessible=True
) -> model.HistoryDatasetCollectionAssociation:
instance_id = trans.app.security.decode_id(id) if isinstance(id, str) else id
collection_instance = trans.sa_session.get(trans.app.model.HistoryDatasetCollectionAssociation, instance_id)
if not collection_instance:
raise RequestParameterInvalidException("History dataset collection association not found")
# TODO: that sure looks like a bug, we can't check ownership using the history of the object we're checking ownership for ...
history = getattr(trans, "history", collection_instance.history)
if check_ownership:
self.history_manager.error_unless_owner(collection_instance.history, trans.user, current_history=history)
if check_accessible:
self.history_manager.error_unless_accessible(
collection_instance.history, trans.user, current_history=history
)
return collection_instance
def __get_library_collection_instance(
self, trans: ProvidesHistoryContext, id, check_ownership=False, check_accessible=True
) -> model.LibraryDatasetCollectionAssociation:
if check_ownership:
raise NotImplementedError(
"Functionality (getting library dataset collection with ownership check) unimplemented."
)
instance_id = int(trans.security.decode_id(id))
collection_instance = trans.sa_session.get(trans.app.model.LibraryDatasetCollectionAssociation, instance_id)
if not collection_instance:
raise RequestParameterInvalidException("Library dataset collection association not found")
if check_accessible:
if not trans.app.security_agent.can_access_library_item(
trans.get_current_user_roles(), collection_instance, trans.user
):
raise ItemAccessibilityException(
"LibraryDatasetCollectionAssociation is not accessible to the current user", type="error"
)
return collection_instance
[docs] def get_collection_contents(self, trans: ProvidesAppContext, parent_id, limit=None, offset=None):
"""Find first level of collection contents by containing collection parent_id"""
contents_qry = self._get_collection_contents_qry(parent_id, limit=limit, offset=offset)
contents = contents_qry.with_session(trans.sa_session()).all()
return contents
def _get_collection_contents_qry(self, parent_id, limit=None, offset=None):
"""Build query to find first level of collection contents by containing collection parent_id"""
DCE = model.DatasetCollectionElement
qry = Query(DCE).filter(DCE.dataset_collection_id == parent_id) # type:ignore[var-annotated]
qry = qry.order_by(DCE.element_index)
qry = qry.options(
joinedload(model.DatasetCollectionElement.child_collection), joinedload(model.DatasetCollectionElement.hda)
)
if limit is not None:
qry = qry.limit(int(limit))
if offset is not None:
qry = qry.offset(int(offset))
return qry
[docs] def write_dataset_collection(self, request: PrepareDatasetCollectionDownload):
short_term_storage_monitor = self.short_term_storage_monitor
instance_id = request.history_dataset_collection_association_id
with storage_context(request.short_term_storage_request_id, short_term_storage_monitor) as target:
collection_instance = self.model.context.get(model.HistoryDatasetCollectionAssociation, instance_id)
with ZipFile(target.path, "w") as zip_f:
write_dataset_collection(collection_instance, zip_f)