def _precreate_fetched_collection_instance(trans, history, target, outputs):
collection_type = target.get("collection_type")
if not collection_type:
# Can't precreate collections of unknown type at this time.
return
name = target.get("name")
if not name:
return
tags = target.get("tags", [])
collections_service = trans.app.dataset_collections_service
collection_type_description = collections_service.collection_type_descriptions.for_collection_type(
collection_type)
structure = UninitializedTree(collection_type_description)
hdca = collections_service.precreate_dataset_collection_instance(
trans, history, name, structure=structure, tags=tags)
outputs.append(hdca)
# Following flushed needed for an ID.
trans.sa_session.flush()
target["destination"]["object_id"] = hdca.id
def collect_dynamic_outputs(
job_context,
output_collections,
):
# unmapped outputs do not correspond to explicit outputs of the tool, they were inferred entirely
# from the tool provided metadata (e.g. galaxy.json).
for unnamed_output_dict in job_context.tool_provided_metadata.get_unnamed_outputs():
assert "destination" in unnamed_output_dict
assert "elements" in unnamed_output_dict
destination = unnamed_output_dict["destination"]
elements = unnamed_output_dict["elements"]
assert "type" in destination
destination_type = destination["type"]
assert destination_type in ["library_folder", "hdca", "hdas"]
# three destination types we need to handle here - "library_folder" (place discovered files in a library folder),
# "hdca" (place discovered files in a history dataset collection), and "hdas" (place discovered files in a history
# as stand-alone datasets).
if destination_type == "library_folder":
# populate a library folder (needs to be already have been created)
library_folder = job_context.get_library_folder(destination)
persist_elements_to_folder(job_context, elements, library_folder)
elif destination_type == "hdca":
# create or populate a dataset collection in the history
assert "collection_type" in unnamed_output_dict
object_id = destination.get("object_id")
if object_id:
hdca = job_context.get_hdca(object_id)
else:
name = unnamed_output_dict.get("name", "unnamed collection")
collection_type = unnamed_output_dict["collection_type"]
collection_type_description = COLLECTION_TYPE_DESCRIPTION_FACTORY.for_collection_type(collection_type)
structure = UninitializedTree(collection_type_description)
hdca = job_context.create_hdca(name, structure)
persist_elements_to_hdca(job_context, elements, hdca, collector=DEFAULT_DATASET_COLLECTOR)
elif destination_type == "hdas":
persist_hdas(elements, job_context)
for name, has_collection in output_collections.items():
output_collection_def = job_context.output_collection_def(name)
if not output_collection_def:
continue
if not output_collection_def.dynamic_structure:
continue
# Could be HDCA for normal jobs or a DC for mapping
# jobs.
if hasattr(has_collection, "collection"):
collection = has_collection.collection
else:
collection = has_collection
# We are adding dynamic collections, which may be precreated, but their actually state is still new!
collection.populated_state = collection.populated_states.NEW
try:
collection_builder = builder.BoundCollectionBuilder(collection)
dataset_collectors = [dataset_collector(description) for description in output_collection_def.dataset_collector_descriptions]
output_name = output_collection_def.name
filenames = job_context.find_files(output_name, collection, dataset_collectors)
job_context.populate_collection_elements(
collection,
collection_builder,
filenames,
name=output_collection_def.name,
metadata_source_name=output_collection_def.metadata_source,
)
collection_builder.populate()
except Exception:
log.exception("Problem gathering output collection.")
collection.handle_population_failed("Problem building datasets for collection.")
job_context.add_dataset_collection(has_collection)