def __expand_collection_parameter(trans, input_key, incoming_val, collections_to_match, linked=False):
# If subcollectin multirun of data_collection param - value will
# be "hdca_id|subcollection_type" else it will just be hdca_id
if "|" in incoming_val:
encoded_hdc_id, subcollection_type = incoming_val.split("|", 1)
else:
try:
src = incoming_val["src"]
if src != "hdca":
raise exceptions.ToolMetaParameterException(f"Invalid dataset collection source type {src}")
encoded_hdc_id = incoming_val["id"]
subcollection_type = incoming_val.get('map_over_type', None)
except TypeError:
encoded_hdc_id = incoming_val
subcollection_type = None
hdc_id = trans.app.security.decode_id(encoded_hdc_id)
hdc = trans.sa_session.query(model.HistoryDatasetCollectionAssociation).get(hdc_id)
collections_to_match.add(input_key, hdc, subcollection_type=subcollection_type, linked=linked)
if subcollection_type is not None:
subcollection_elements = subcollections.split_dataset_collection_instance(hdc, subcollection_type)
return subcollection_elements
else:
hdas = []
for element in hdc.collection.dataset_elements:
hda = element.dataset_instance
hda.element_identifier = element.element_identifier
hdas.append(hda)
return hdas
def collection_classifier(input_key):
multirun_key = "%s|__collection_multirun__" % input_key
if multirun_key in incoming:
incoming_val = incoming[multirun_key]
# If subcollectin multirun of data_collection param - value will
# be "hdca_id|subcollection_type" else it will just be hdca_id
if "|" in incoming_val:
encoded_hdc_id, subcollection_type = incoming_val.split("|", 1)
else:
try:
src = incoming_val["src"]
if src != "hdca":
raise exceptions.ToolMetaParameterException(
"Invalid dataset collection source type %s" % src)
encoded_hdc_id = incoming_val["id"]
except TypeError:
encoded_hdc_id = incoming_val
subcollection_type = None
hdc_id = trans.app.security.decode_id(encoded_hdc_id)
hdc = trans.sa_session.query(
model.HistoryDatasetCollectionAssociation).get(hdc_id)
collections_to_match.add(input_key,
hdc,
subcollection_type=subcollection_type)
if subcollection_type is not None:
from galaxy.dataset_collections import subcollections
subcollection_elements = subcollections.split_dataset_collection_instance(
hdc, subcollection_type)
return permutations.input_classification.MATCHED, subcollection_elements
else:
hdas = hdc.collection.dataset_instances
return permutations.input_classification.MATCHED, hdas
else:
return permutations.input_classification.SINGLE, incoming[
input_key]
def expand_meta_parameters(trans, tool, incoming):
"""
Take in a dictionary of raw incoming parameters and expand to a list
of expanded incoming parameters (one set of parameters per tool
execution).
"""
def classifier(input_key):
multirun_key = "%s|__multirun__" % input_key
if multirun_key in incoming:
multi_value = util.listify(incoming[multirun_key])
if len(multi_value) > 1:
return permutations.input_classification.MATCHED, multi_value
else:
if len(multi_value) == 0:
multi_value = None
return permutations.input_classification.SINGLE, multi_value[0]
else:
return permutations.input_classification.SINGLE, incoming[
input_key]
from galaxy.dataset_collections import matching
collections_to_match = matching.CollectionsToMatch()
def collection_classifier(input_key):
multirun_key = "%s|__collection_multirun__" % input_key
if multirun_key in incoming:
incoming_val = incoming[multirun_key]
# If subcollectin multirun of data_collection param - value will
# be "hdca_id|subcollection_type" else it will just be hdca_id
if "|" in incoming_val:
encoded_hdc_id, subcollection_type = incoming_val.split("|", 1)
else:
try:
src = incoming_val["src"]
if src != "hdca":
raise exceptions.ToolMetaParameterException(
"Invalid dataset collection source type %s" % src)
encoded_hdc_id = incoming_val["id"]
except TypeError:
encoded_hdc_id = incoming_val
subcollection_type = None
hdc_id = trans.app.security.decode_id(encoded_hdc_id)
hdc = trans.sa_session.query(
model.HistoryDatasetCollectionAssociation).get(hdc_id)
collections_to_match.add(input_key,
hdc,
subcollection_type=subcollection_type)
if subcollection_type is not None:
from galaxy.dataset_collections import subcollections
subcollection_elements = subcollections.split_dataset_collection_instance(
hdc, subcollection_type)
return permutations.input_classification.MATCHED, subcollection_elements
else:
hdas = hdc.collection.dataset_instances
return permutations.input_classification.MATCHED, hdas
else:
return permutations.input_classification.SINGLE, incoming[
input_key]
# Stick an unexpanded version of multirun keys so they can be replaced,
# by expand_mult_inputs.
incoming_template = incoming.copy()
# Will reuse this in subsequent work, so design this way now...
def try_replace_key(key, suffix):
found = key.endswith(suffix)
if found:
simple_key = key[0:-len(suffix)]
if simple_key not in incoming_template:
incoming_template[simple_key] = None
return found
multirun_found = False
collection_multirun_found = False
for key, value in incoming.iteritems():
multirun_found = try_replace_key(key,
"|__multirun__") or multirun_found
collection_multirun_found = try_replace_key(
key, "|__collection_multirun__") or collection_multirun_found
if sum(
[1 if f else 0
for f in [multirun_found, collection_multirun_found]]) > 1:
# In theory doable, but to complicated for a first pass.
message = "Cannot specify parallel execution across both multiple datasets and dataset collections."
raise exceptions.ToolMetaParameterException(message)
if multirun_found:
return permutations.expand_multi_inputs(incoming_template,
classifier), None
else:
expanded_incomings = permutations.expand_multi_inputs(
incoming_template, collection_classifier)
if collections_to_match.has_collections():
collection_info = trans.app.dataset_collections_service.match_collections(
collections_to_match)
else:
collection_info = None
return expanded_incomings, collection_info