def write_job_metadata(tool_job_working_directory, job_metadata, set_meta, tool_provided_metadata):
for i, file_dict in enumerate(tool_provided_metadata.get_new_datasets_for_metadata_collection(), start=1):
filename = file_dict["filename"]
new_dataset_filename = os.path.join(tool_job_working_directory, "working", filename)
new_dataset = Dataset(id=-i, external_filename=new_dataset_filename)
extra_files = file_dict.get('extra_files', None)
if extra_files is not None:
new_dataset._extra_files_path = os.path.join(tool_job_working_directory, "working", extra_files)
new_dataset.state = new_dataset.states.OK
new_dataset_instance = HistoryDatasetAssociation(id=-i, dataset=new_dataset, extension=file_dict.get('ext', 'data'))
set_meta(new_dataset_instance, file_dict)
file_dict['metadata'] = json.loads(new_dataset_instance.metadata.to_JSON_dict()) # storing metadata in external form, need to turn back into dict, then later jsonify
tool_provided_metadata.rewrite()
def _job_dataset(self, name, path):
metadata = dict()
hda = HistoryDatasetAssociation(name=name, metadata=metadata)
hda.dataset = Dataset(id=123, external_filename=path)
hda.dataset.metadata = dict()
hda.children = []
jida = JobToInputDatasetAssociation(name=name, dataset=hda)
return jida
def hda(id, name, path):
hda = HistoryDatasetAssociation(name=name, metadata=dict())
hda.dataset = Dataset(id=id, external_filename=path)
hda.dataset.metadata = dict()
hda.children = []
return hda