def __populate_output_dataset_wrappers(self, param_dict, output_datasets, output_paths, job_working_directory):
output_dataset_paths = dataset_path_rewrites(output_paths)
for name, hda in output_datasets.items():
# Write outputs to the working directory (for security purposes)
# if desired.
real_path = hda.file_name
if real_path in output_dataset_paths:
dataset_path = output_dataset_paths[real_path]
param_dict[name] = DatasetFilenameWrapper(hda, dataset_path=dataset_path)
try:
open(dataset_path.false_path, 'w').close()
except EnvironmentError:
pass # May well not exist - e.g. Pulsar.
else:
param_dict[name] = DatasetFilenameWrapper(hda)
# Provide access to a path to store additional files
# TODO: path munging for cluster/dataset server relocatability
store_by = getattr(hda.dataset.object_store, "store_by", "id")
file_name = "dataset_%s_files" % getattr(hda.dataset, store_by)
param_dict[name].files_path = os.path.abspath(os.path.join(job_working_directory, "working", file_name))
for out_name, output in self.tool.outputs.items():
if out_name not in param_dict and output.filters:
# Assume the reason we lack this output is because a filter
# failed to pass; for tool writing convienence, provide a
# NoneDataset
ext = getattr(output, "format", None) # populate only for output datasets (not collections)
param_dict[out_name] = NoneDataset(datatypes_registry=self.app.datatypes_registry, ext=ext)
def __populate_output_dataset_wrappers(self, param_dict, output_datasets, job_working_directory):
for name, hda in output_datasets.items():
# Write outputs to the working directory (for security purposes)
# if desired.
param_dict[name] = DatasetFilenameWrapper(hda, compute_environment=self.compute_environment, io_type="output")
output_path = str(param_dict[name])
# Conditionally create empty output:
# - may already exist (e.g. symlink output)
# - parent directory might not exist (e.g. Pulsar)
if not os.path.exists(output_path) and os.path.exists(os.path.dirname(output_path)):
open(output_path, 'w').close()
# Provide access to a path to store additional files
# TODO: move compute path logic into compute environment, move setting files_path
# logic into DatasetFilenameWrapper. Currently this sits in the middle and glues
# stuff together inconsistently with the way the rest of path rewriting works.
file_name = hda.dataset.extra_files_path_name
param_dict[name].files_path = os.path.abspath(os.path.join(job_working_directory, "working", file_name))
for out_name, output in self.tool.outputs.items():
if out_name not in param_dict and output.filters:
# Assume the reason we lack this output is because a filter
# failed to pass; for tool writing convienence, provide a
# NoneDataset
ext = getattr(output, "format", None) # populate only for output datasets (not collections)
param_dict[out_name] = NoneDataset(datatypes_registry=self.app.datatypes_registry, ext=ext)
def __init__(self,
dataset,
datatypes_registry=None,
tool=None,
name=None,
compute_environment=None,
identifier=None,
io_type="input",
formats=None):
if not dataset:
try:
# TODO: allow this to work when working with grouping
ext = tool.inputs[name].extensions[0]
except Exception:
ext = 'data'
self.dataset = wrap_with_safe_string(
NoneDataset(datatypes_registry=datatypes_registry, ext=ext),
no_wrap_classes=ToolParameterValueWrapper)
else:
# Tool wrappers should not normally be accessing .dataset directly,
# so we will wrap it and keep the original around for file paths
# Should we name this .value to maintain consistency with most other ToolParameterValueWrapper?
if formats:
target_ext, converted_dataset = dataset.find_conversion_destination(
formats)
if target_ext and converted_dataset:
dataset = converted_dataset
self.unsanitized = dataset
self.dataset = wrap_with_safe_string(
dataset, no_wrap_classes=ToolParameterValueWrapper)
self.metadata = self.MetadataWrapper(dataset, compute_environment)
if hasattr(dataset, 'tags'):
self.groups = {
tag.user_value.lower()
for tag in dataset.tags if tag.user_tname == 'group'
}
else:
# May be a 'FakeDatasetAssociation'
self.groups = set()
self.compute_environment = compute_environment
# TODO: lazy initialize this...
self.__io_type = io_type
if self.__io_type == "input":
path_rewrite = compute_environment and dataset and compute_environment.input_path_rewrite(
dataset)
if path_rewrite:
self.false_path = path_rewrite
else:
self.false_path = None
else:
path_rewrite = compute_environment and compute_environment.output_path_rewrite(
dataset)
if path_rewrite:
self.false_path = path_rewrite
else:
self.false_path = None
self.datatypes_registry = datatypes_registry
self._element_identifier = identifier
def __init__(
self,
dataset: Optional[DatasetInstance],
datatypes_registry: Optional["Registry"] = None,
tool: Optional["Tool"] = None,
name: Optional[str] = None,
compute_environment: Optional["ComputeEnvironment"] = None,
identifier: Optional[str] = None,
io_type: str = "input",
formats: Optional[List[str]] = None,
) -> None:
if not dataset:
try:
# TODO: allow this to work when working with grouping
ext = tool.inputs[name].extensions[
0] # type: ignore[union-attr]
except Exception:
ext = "data"
self.dataset = cast(
DatasetInstance,
wrap_with_safe_string(
NoneDataset(datatypes_registry=datatypes_registry,
ext=ext),
no_wrap_classes=ToolParameterValueWrapper,
),
)
else:
# Tool wrappers should not normally be accessing .dataset directly,
# so we will wrap it and keep the original around for file paths
# Should we name this .value to maintain consistency with most other ToolParameterValueWrapper?
if formats:
direct_match, target_ext, converted_dataset = dataset.find_conversion_destination(
formats)
if not direct_match and target_ext and converted_dataset:
dataset = converted_dataset
self.unsanitized: DatasetInstance = dataset
self.dataset = wrap_with_safe_string(
dataset, no_wrap_classes=ToolParameterValueWrapper)
assert dataset
self.metadata = self.MetadataWrapper(dataset, compute_environment)
if isinstance(dataset, HasTags):
self.groups = {
tag.user_value.lower()
for tag in dataset.tags if tag.user_tname == 'group'
}
else:
# May be a 'FakeDatasetAssociation'
self.groups = set()
self.compute_environment = compute_environment
# TODO: lazy initialize this...
self.__io_type = io_type
if self.__io_type == "input":
path_rewrite = compute_environment and dataset and compute_environment.input_path_rewrite(
dataset)
if path_rewrite:
self.false_path = path_rewrite
else:
self.false_path = None
else:
path_rewrite = compute_environment and compute_environment.output_path_rewrite(
dataset)
if path_rewrite:
self.false_path = path_rewrite
else:
self.false_path = None
self.datatypes_registry = datatypes_registry
self._element_identifier = identifier